[Engine] Apply the STS task to bge models (#673)

examples/huggingface/pytorch/text-classification/deployment/mrpc/bge_large/README.md → examples/huggingface/pytorch/text-embedding/deployment/mteb/bge/README.md

@@ -1,6 +1,6 @@
 Step-by-Step
 =======
-This document describes the end-to-end workflow for Huggingface model [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) and [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) with Neural Engine backend.
+This document describes the end-to-end workflow for Huggingface model [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5), [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) and [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) with Neural Engine backend.
 
 Here we take the [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) as an example.
 
@@ -53,15 +53,15 @@ Neural Engine can parse ONNX model and Neural Engine IR.
 We provide with three `modes`: `accuracy`, `throughput` or `latency`. For throughput mode, we will use multi-instance with 4cores/instance occupying one socket.
 You can run fp32 model inference by setting `precision=fp32`, command as follows:
 ```shell
-bash run_bge.sh --model=BAAI/bge-base-en-v1.5  --dataset=mrpc --precision=fp32 --mode=throughput
+bash run_bge.sh --model=BAAI/bge-base-en-v1.5 --precision=fp32 --mode=throughput
 ```
 By setting `precision=int8` you could get PTQ int8 model and setting `precision=bf16` to get bf16 model.
 ```shell
-bash run_bge.sh --model=BAAI/bge-base-en-v1.5  --dataset=mrpc --precision=int8 --mode=throughput
+bash run_bge.sh --model=BAAI/bge-base-en-v1.5 --precision=int8 --mode=throughput
 ```
 By setting `precision=dynamic_int8`, you could benchmark dynamic quantized int8 model.
 ```shell
-bash run_bge.sh --model=BAAI/bge-base-en-v1.5  --dataset=mrpc --precision=dynamic_int8 --mode=throughput
+bash run_bge.sh --model=BAAI/bge-base-en-v1.5 --precision=dynamic_int8 --mode=throughput
 ```
 
 

examples/huggingface/pytorch/text-embedding/deployment/mteb/bge/engine_model.py

@@ -0,0 +1,177 @@
+from typing import cast, List, Dict, Union
+
+import numpy as np
+import torch
+from mteb import DRESModel
+from tqdm import tqdm
+from transformers import AutoModel, AutoTokenizer
+from optimum.onnxruntime import ORTModelForFeatureExtraction
+import copy
+
+
+class EngineBGEModel(DRESModel):
+    def __init__(self,
+                 model_name_or_path: str = None,
+                 pooling_method: str = 'cls',
+                 normalize_embeddings: bool = True,
+                 query_instruction_for_retrieval: str = None,
+                 batch_size: int = 256,
+                 backend: str = 'Engine',
+                 **kwargs) -> None:
+
+        ort_model_path = kwargs.get("ort_model_path", None)
+        engine_model = kwargs.get("engine_model", None)
+        self.backend = kwargs.get("backend", 'Engine')
+
+        if backend == 'Engine':
+            self.engine_model = engine_model.graph
+            file_name = kwargs.get("file_name", None)
+            print('The backend is Neural Engine, evaluate on: ', ort_model_path, file_name)
+            self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+            self.pytorch_model = AutoModel.from_pretrained(model_name_or_path)
+            self.hidden_size = self.pytorch_model.config.hidden_size
+        elif backend == 'Pytorch':
+            print('The backend is Pytorch, evaluate on: ', ort_model_path, file_name)
+            self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+            self.pytorch_model = AutoModel.from_pretrained(model_name_or_path)
+        elif backend == 'Onnxruntime':
+            print('The backend is Onnxruntime.')
+            file_name = kwargs.get("file_name", None)
+            print('The backend is Onnxruntime, evaluate on: ', ort_model_path, file_name)
+            self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
+            self.ort_model = ORTModelForFeatureExtraction.from_pretrained(ort_model_path, file_name=file_name)
+
+        self.query_instruction_for_retrieval = query_instruction_for_retrieval
+        self.normalize_embeddings = normalize_embeddings
+        self.pooling_method = pooling_method
+        self.batch_size = batch_size
+        self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+
+        if self.pytorch_model is not None:
+            self.pytorch_model = self.pytorch_model.to(self.device)
+
+            num_gpus = torch.cuda.device_count()
+            if num_gpus > 1:
+                self.pytorch_model = torch.nn.DataParallel(self.pytorch_model)
+                self.batch_size = self.batch_size * num_gpus
+
+    def encode_queries(self, queries: List[str], **kwargs) -> np.ndarray:
+        '''
+        This function will be used for retrieval task
+        if there is a instruction for queries, we will add it to the query text
+        '''
+        if self.query_instruction_for_retrieval is not None:
+            input_texts = ['{}{}'.format(self.query_instruction_for_retrieval, q) for q in queries]
+        else:
+            input_texts = queries
+        return self.encode(input_texts)
+
+    def encode_corpus(self, corpus: List[Union[Dict[str, str], str]], **kwargs) -> np.ndarray:
+        '''
+        This function will be used for retrieval task
+        encode corpus for retrieval task
+        '''
+        if isinstance(corpus[0], dict):
+            input_texts = ['{} {}'.format(doc.get('title', ''), doc['text']).strip() for doc in corpus]
+        else:
+            input_texts = corpus
+        return self.encode(input_texts)
+
+    @torch.no_grad()
+    def encode(self, sentences: List[str], **kwargs) -> np.ndarray:
+        if self.backend == 'Engine':
+            ort_all_embeddings = []
+            for start_index in tqdm(range(0, len(sentences), self.batch_size),
+                                    desc="Batches",
+                                    disable=len(sentences) < 256):
+                sentences_batch = sentences[start_index:start_index + self.batch_size]
+                inputs = self.tokenizer(
+                    sentences_batch,
+                    padding=True,
+                    truncation=True,
+                    return_tensors='pt',
+                    max_length=512,
+                ).to(self.device)
+
+                ort_inputs = self.tokenizer(sentences_batch,
+                                            padding=True,
+                                            truncation=True,
+                                            max_length=512,
+                                            return_tensors="np")
+
+                input_ids = np.ascontiguousarray(ort_inputs['input_ids'])
+                token_type_ids = np.ascontiguousarray(ort_inputs['token_type_ids'])
+                attention_mask = np.ascontiguousarray(ort_inputs['attention_mask'])
+
+                engine_input = [input_ids, token_type_ids, attention_mask]
+                result = copy.deepcopy(self.engine_model.inference(engine_input))
+                ort_last_hidden_state = torch.tensor(result['last_hidden_state:0']).reshape(
+                    input_ids.shape[0], input_ids.shape[1], self.hidden_size)
+                ort_embeddings = self.pooling(ort_last_hidden_state, inputs['attention_mask'])
+                if self.normalize_embeddings:
+                    ort_embeddings = torch.nn.functional.normalize(ort_embeddings, dim=-1)
+                ort_embeddings = cast(torch.Tensor, ort_embeddings)
+                ort_all_embeddings.append(ort_embeddings.cpu().numpy())
+            return np.concatenate(ort_all_embeddings, axis=0)
+        elif self.backend == 'Pytorch':
+            self.pytorch_model.eval()
+            all_embeddings = []
+            for start_index in tqdm(range(0, len(sentences), self.batch_size),
+                                    desc="Batches",
+                                    disable=len(sentences) < 256):
+                sentences_batch = sentences[start_index:start_index + self.batch_size]
+                inputs = self.tokenizer(
+                    sentences_batch,
+                    padding=True,
+                    truncation=True,
+                    return_tensors='pt',
+                    max_length=512,
+                ).to(self.device)
+                ort_inputs = self.tokenizer(sentences_batch,
+                                            padding=True,
+                                            truncation=True,
+                                            max_length=512,
+                                            return_tensors="np")
+                last_hidden_state = self.pytorch_model(**inputs, return_dict=True).last_hidden_state
+                embeddings = self.pooling(last_hidden_state, inputs['attention_mask'])
+                if self.normalize_embeddings:
+                    embeddings = torch.nn.functional.normalize(embeddings, dim=-1)
+                embeddings = cast(torch.Tensor, embeddings)
+                all_embeddings.append(embeddings.cpu().numpy())
+            return np.concatenate(all_embeddings, axis=0)
+
+        elif self.backend == 'Onnxruntime':
+            ort_all_embeddings = []
+            for start_index in tqdm(range(0, len(sentences), self.batch_size),
+                                    desc="Batches",
+                                    disable=len(sentences) < 256):
+                sentences_batch = sentences[start_index:start_index + self.batch_size]
+                inputs = self.tokenizer(
+                    sentences_batch,
+                    padding=True,
+                    truncation=True,
+                    return_tensors='pt',
+                    max_length=512,
+                ).to(self.device)
+
+                ort_inputs = self.tokenizer(sentences_batch,
+                                            padding=True,
+                                            truncation=True,
+                                            max_length=512,
+                                            return_tensors="np")
+
+                ort_last_hidden_state = torch.tensor(self.ort_model(**ort_inputs).last_hidden_state)
+                ort_embeddings = self.pooling(ort_last_hidden_state, inputs['attention_mask'])
+                if self.normalize_embeddings:
+                    ort_embeddings = torch.nn.functional.normalize(ort_embeddings, dim=-1)
+                ort_embeddings = cast(torch.Tensor, ort_embeddings)
+                ort_all_embeddings.append(ort_embeddings.cpu().numpy())
+            return np.concatenate(ort_all_embeddings, axis=0)
+
+    def pooling(self, last_hidden_state: torch.Tensor, attention_mask: torch.Tensor = None):
+        if self.pooling_method == 'cls':
+            return last_hidden_state[:, 0]
+        elif self.pooling_method == 'mean':
+            s = torch.sum(last_hidden_state * attention_mask.unsqueeze(-1).float(), dim=1)
+            d = attention_mask.sum(dim=1, keepdim=True).float()
+            return s / d

examples/huggingface/pytorch/text-classification/deployment/mrpc/bge_large/executor_utils.py → examples/huggingface/pytorch/text-embedding/deployment/mteb/bge/executor_utils.py

@@ -20,15 +20,46 @@
 from tqdm import tqdm
 from datasets import load_metric
 from executor_dataloader import DataLoader
+from intel_extension_for_transformers.llm.runtime.deprecated.compile import compile, autocast
+from intel_extension_for_transformers.llm.runtime.deprecated.compile.graph import Graph
 import sys
 import os
+import logging
 
 common_dir = os.path.join(sys.path[0], "../../../../neural_engine_utils/")
 sys.path.append(common_dir)
-from common import (log, DummyDataLoader, compute_performance, Neural_Engine_base)
+from common import (log, DummyDataLoader, compute_performance)
 
 
-class Neural_Engine(Neural_Engine_base):
+# set log file
+def set_log_file(log, log_file):
+    file_handler = logging.FileHandler(log_file, 'w')
+    formatter = logging.Formatter('%(asctime)s [%(levelname)s] %(message)s', "%Y-%m-%d %H:%M:%S")
+    file_handler.setFormatter(formatter)
+    log.addHandler(file_handler)
+
+bge_pattern_config = {
+    'pattern_switch': {
+        'MultiHeadAttention': False,
+    }
+}
+
+class Neural_Engine_bge():
+
+    def __init__(self, model_path, log_file, cast_type="native"):
+        set_log_file(log, log_file)
+        with autocast(cast_type):
+            self.graph = compile(model_path, bge_pattern_config)
+        self.log_file = log_file
+
+    def accuracy(self, batch_size, seq_len, dataset_name, task_name, data_dir, tokenizer_dir):
+        pass
+
+    def performance(self, batch_size, seq_len, iteration, warm_up):
+        pass
+
+
+class Neural_Engine(Neural_Engine_bge):
 
     def accuracy(self, batch_size, seq_len, dataset_name, task_name, data_dir, tokenizer_dir):
         # load dataset

examples/huggingface/pytorch/text-classification/deployment/mrpc/bge_large/requirements.txt → examples/huggingface/pytorch/text-embedding/deployment/mteb/bge/requirements.txt

@@ -1,10 +1,11 @@
 neural-compressor
-transformers==4.34.1
 accelerate
 datasets >= 1.8.0
 sentencepiece != 0.1.92
 protobuf
 torch==2.1.0
-onnx>=1.12
+onnx==1.13.1
 onnxruntime==1.13.1
-
+mteb==1.1.1
+beir
+transformers==4.34.1